Detecting out-of-distribution (OOD) inputs is crucial for the safe deployment
of natural language processing (NLP) models. Though existing methods,
especially those based on the statistics in the feature space of fine-tuned
pre-trained language models (PLMs), are claimed to be effective, their
effectiveness on different types of distribution shifts remains underexplored.
In this work, we take the first step to comprehensively evaluate the mainstream
textual OOD detection methods for detecting semantic and non-semantic shifts.
We find that: (1) no existing method behaves well in both settings; (2)
fine-tuning PLMs on in-distribution data benefits detecting semantic shifts but
severely deteriorates detecting non-semantic shifts, which can be attributed to
the distortion of task-agnostic features. To alleviate the issue, we present a
simple yet effective general OOD score named GNOME that integrates the
confidence scores derived from the task-agnostic and task-specific
representations. Experiments show that GNOME works well in both semantic and
non-semantic shift scenarios, and further brings significant improvement on two
cross-task benchmarks where both kinds of shifts simultaneously take place. Our
code is available at this https URL.

本论文探讨了文本 out-of-distribution detection 的方法在检测 semantic 和 non-semantic shifts 时存在的问题，并提出了一个简单有效的综合方法 GNOME，通过整合 task-agnostic 和 task-specific representations 的置信度得分来检测语义和非语义的偏移，实验证明 GNOME 在两个跨任务基准测试中表现出了明显的改进。